We built RapidFire AI because iterating on RAG pipelines is painfully sequential: run a config, wait, inspect results, tweak one knob, repeat. When you have 15 things to tune (chunk size, retrieval k, reranker, prompt template, context window strategy...) that cycle compounds fast.

RapidFire uses shard-based interleaved scheduling to run many configurations concurrently on a single machine — even a CPU-only box if you're using a closed API like OpenAI. Instead of config A finishing before config B starts, all configs process data shards in rotation, so you see live side-by-side metric deltas within the first few minutes.

The part we're most excited about: Interactive Control (IC Ops).

Most RAG observability tools tell you what happened after a run finishes. IC Ops closes the loop — you can act on what you're observing mid-run:

  - Stop a config that's clearly underperforming (save the API spend)
  - Resume it later if you change your mind
  - Clone a promising run and modify its prompt template or retrieval 
    strategy on the fly, with or without warm-starting from the parent's state

This changes the experimentation workflow from "observe → write notes → re-queue a new job" to "observe → fix → continue" in a single session.

What you can experiment over in one run: - Chunking strategy and overlap - Embedding model - Retrieval k and hybrid search weighting - Reranking model / threshold - Prompt template variants (few-shot, CoT, context compression) - Generation model (swap GPT-4o vs Claude 3.5 vs local model mid-experiment)

Eval metrics aggregate online (no need to wait for full run), displayed in a live-updating in-notebook table. Full MLflow integration for longer-term experiment governance.

GitHub: https://github.com/RapidFireAI/rapidfireai

Docs: https://oss-docs.rapidfire.ai

pip install rapidfireai

Show HN: RapidFire AI – parallel RAG experimentation with live run intervention